Method and apparatus of sharing information related to status

ABSTRACT

The present invention relates to method and device for sharing state related information among a plurality of electronic devices and, more particularly, to method and device for predicting the state of a device on the basis of information shared among a plurality of electronic devices. In order to attain the purpose, a method for sharing state related information of a device, according to an embodiment of the present invention, comprises the steps of: generating a state model of a device on the basis of state related data; selecting one or more parameters for determining the state of the device on the basis of the generated state model; and transmitting the one or more selected parameters to at least one other device.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation application of prior application Ser.No. 17/105,840 filed on Nov. 27, 2020, which has issued a U.S. Pat. No.11,758,415 on Sep. 12, 2023; which is a continuation application of aprior application Ser. No. 15/776,528 filed on May 16, 2018, which hasissued as U.S. Pat. No. 10,880,758 on Dec. 29, 2020; which is a U.S.National Stage application under 35 U.S.C. § 371 of an Internationalapplication number PCT/KR2016/013347 filed on Nov. 18, 2016, which isbased on and claimed priority of a Korean patent application number10-2015-0163556 filed on Nov. 20, 2015 in the Korean IntellectualProperty Office, the disclosure of each of which is incorporated byreference herein in its entirety.

TECHNICAL FIELD

The present invention relates to a method and apparatus for sharingstate information between a plurality of electronic devices, and moreparticularly, to a method and apparatus for predicting the state of adevice based on information shared among multiple electronic devices.

BACKGROUND ART

Recently, with the increase of mobile communication devices and the useof big data and cloud computing technologies, traffic has been rapidlyincreasing. In this trend, low-latency, high-throughput and secureend-to-end communication has become important. At the same time, inparticular, as the role of the base station system (BSS) equipmentrelaying such communication becomes important, there is an ongoingdiscussion on the analysis capability for continuous real-time resourceallocation, performance optimization, stability, and the cause ofanomalies. In general, to satisfy the requirements of a mobilecommunication service provider, values for quality of service (QoS),quality of experience (QoE), and service level agreement (SLA) aredefined, and mobile communication devices are operated based on them.

In operating the mobile communication apparatus, the state predictionrefers to predicting the software and hardware state of the apparatus inthe future on the basis of the previous operational log information ofthe mobile communication apparatus. As a specific example, the states inthe mobile communication apparatus may include the state of networkresource distribution, the state of power usage, and the state ofmaintaining throughput connection.

For the state prediction of the performance apparatus, sensor data ofvarious elements that can be extracted from the apparatus can becollected. For example, statistical analysis and machine learningtechniques can be applied to the sensor data to predict the state of theapparatus. The sensor data can be classified into periodic data, eventdata, and configuration data according to the data collection approach.For example, the apparatus may collect periodic data by periodicallyrecording information about elements extracted from software andhardware, such as temperature, power interference, and resourceutilization. The apparatus may collect event data by, for example,configuring a situation where a certain element exceeds a presetthreshold as an event. The apparatus may collect configuration data byrecording information on the firmware version, location, and cell setupthereof.

FIGS. 1A and 1B illustrate the use of a prediction model for aparticular state of the apparatus.

In FIG. 1A, to make a prediction on the state, the apparatus collectsthe data log during the learning period 100 and generates a predictionmodel 110 based on the collected data logs. Then, the apparatus predictsthe state thereof during the state prediction period 120 by inputtingdata logs of a given period into the prediction model 110.

FIG. 1B shows a process of deriving a prediction result based on theinternal structure of the apparatus making a prediction on the stateusing a prediction model. More specifically, after collecting data logs,the apparatus produces a prediction result through a learning stage inthe modeling unit (MU) and a prediction stage in the prediction unit(PU). The modeling unit and the prediction unit are based on machinelearning algorithms widely known in the art, and the machine learningalgorithms include naive Bayesian networks, support vector machines, andrandom forests. The prediction accuracy according to the predictionresults produced by the listed algorithms may be different depending onthe characteristics and amount of the data logs.

Next, a description is given of the modeling unit and the predictionunit.

First, the modeling unit creates a model using data and data classes.The data may include raw data or processed data logs. In the presentinvention, the data and the data log can be interchangeably used. Thedata class refers to the desired output value for the data. For example,the data class may refer to the result values for data values belongingto a specific period in the past. The model is generated based onstochastic or deterministic patterns of data for the data class.

Next, after the model is generated, the prediction unit inputs a newdata log to the model to produce an output value as a prediction result.That is, the prediction unit derives a data class for the new data logusing a class decision rule of the model. The prediction unit alsoderives the prediction accuracy of the prediction result. The predictionaccuracy can be different depending on the machine learning algorithm,the characteristics and quantity of data logs, the number of parameters,and the data processing precision. The prediction accuracy can beimproved by using feature engineering or feature selection. Inparticular, it is possible to extract and learn various patterns as thenumber of learning data logs and the number of parameters increase, sothat collecting and learning data logs of various parameters can improvethe prediction accuracy. Feature engineering or feature selectionarranging modeling, prediction, and data classes for a typical machinelearning operation is a general technique in the field of the presentinvention and does not belong to the scope of the present invention, anda detailed description thereof will be omitted.

FIG. 2 illustrates a specific method of applying data learning and theprediction model.

More specifically, a description is given of a decentralized method anda centralized method, which are methods for, e.g., plural base stationsthat learn data and generate a prediction model to produce a predictionresult.

In the decentralized method shown in part (a) of FIG. 2 , each basestation learns generated data independently to generate a predictionmodel, and performs data prediction based on the generated predictionmodel. Each base station also calculates the accuracy of the predictionmodel. That is, one base station does not transmit or receive a data logto or from another base station. In the centralized method shown in part(b) of FIG. 2 , each base station transmits a data log generated thereatto the central server, which learns the collected data logs andgenerates a prediction model. The central server performs dataprediction based on the prediction model and calculates the accuracy ofthe prediction model. That is, new data logs are also transmitted to theapparatus and the accuracy is calculated.

More specifically, in the decentralized method, each apparatus learnsand predicts using independently collected data logs. This makes itpossible to create a prediction model by taking into consideration thecharacteristics of each apparatus, but it is necessary to accumulatedata logs for a long period of time for practical use. In addition,since a model can be generated by having information on the output valuefor a data log (i.e., data class), the state prediction for a newlyinstalled apparatus may be impossible because there are no accumulateddata logs.

In the centralized method, the central server can collect a large amountof data logs from various base stations and reach a high predictionaccuracy by using recently introduced big data technology. However, ifthe amount of data increases according to a specific learning algorithm,more resources are needed for learning. In addition, the CPU, memory,and disk capacity requirements of the central server increase; thelearning time becomes long; it is difficult to transmit the predictionresult in real time; and it is difficult to reflect characteristics ofeach base station in the prediction model.

DISCLOSURE OF INVENTION Technical Problem

The present invention has been made in view of the above problems.Accordingly, an aspect of the present invention is to provide a methodfor sharing state related information including parameters selectedbased on a device state model between different devices having similarcharacteristics.

Solution to Problem

In accordance with an aspect of the present invention, there is provideda method of sharing state related information for a device. The methodmay include: generating a state model of the device on the basis ofstate related data; selecting at least one parameter determining thestate of the device based on the generated state model; and transmittingthe selected at least one parameter to at least one different device.

In accordance with another aspect of the present invention, there isprovided a device capable of sharing state related information. Thedevice may include: a transceiver unit configured to transmit andreceive a signal; and a controller configured to control generating astate model of the device on the basis of state related data, selectingat least one parameter determining the state of the device based on thegenerated state model, and transmitting the selected at least oneparameter to at least one different device.

Advantageous Effects of Invention

In a feature of the present invention, state related informationincluding parameters selected based on the state model, other than theentire data logs, is shared among different devices having similarcharacteristics. Hence, it is possible to use a small amount ofresources among the devices. Each device can make a prediction even if aspecific state to be predicted at the present time point has notoccurred in the past. A high prediction accuracy can be achieved bylearning a small amount of data logs. In addition, one device cancombine the state related information received from another device withthe state model generated by it to produce a prediction result. Hence,each device can obtain a prediction result in real time in considerationof the characteristics of the device.

BRIEF DESCRIPTION OF DRAWINGS

FIGS. 1A and 1B illustrate the use of a prediction model for aparticular state of the apparatus.

FIG. 2 illustrates a specific method of applying data learning and theprediction model.

FIG. 3 is a block diagram illustrating the internal structure andoperation of a device according to an embodiment of the presentinvention.

FIG. 4 illustrates pieces of information transmitted and receivedbetween the internal components of the device according to an embodimentof the present invention.

FIGS. 5A and 5B illustrate grouping of devices to share state relatedinformation according to an embodiment of the present invention.

FIGS. 6A and 6B illustrate producing a prediction result for the deviceinstallation state and performing a feedback operation according to anembodiment of the present invention.

FIG. 7 shows a graph representing the state model that predicts thedevice state.

FIGS. 8A, 8B, and 8C illustrate operations for selecting parameters tobe shared with another device for a state model and determining whetherto share the state model according to an embodiment of the presentinvention.

FIG. 9 illustrates the operation of determining whether to share staterelated information with another device according to an embodiment ofthe present invention.

FIG. 10 illustrates an operation by which the device derives aprediction result based on state related information according to anembodiment of the present invention.

FIG. 11 illustrates a feedback operation performed by the device basedon prediction results according to an embodiment of the presentinvention.

MODE FOR THE INVENTION

Hereinafter, preferred embodiments of the present invention aredescribed in detail with reference to the accompanying drawings. Thesame or similar reference symbols are used throughout the drawings torefer to the same or like parts. Descriptions of well-known functionsand constructions may be omitted to avoid obscuring the subject matterof the present invention.

Hereinafter, preferred embodiments of the present invention aredescribed in detail with reference to the accompanying drawings. Thesame or similar reference symbols are used throughout the drawings torefer to the same or like parts. Descriptions of well-known functionsand constructions may be omitted to avoid obscuring the subject matterof the present invention.

The following description is focused on 4G communication systemsincluding the advanced E-UTRA (or LTE-A) system supporting carrieraggregation. However, it should be understood by those skilled in theart that the subject matter of the present invention is applicable toother communication systems having similar technical backgrounds andchannel configurations without significant modifications departing fromthe scope of the present invention. For example, the subject matter ofthe present invention is applicable to multicarrier HSPA systemssupporting carrier aggregation and next generation 5G communicationsystems.

Descriptions of functions and structures well known in the art and notdirectly related to the present invention may also be omitted forclarity and conciseness without obscuring the subject matter of thepresent invention.

In the drawings, some elements are exaggerated, omitted, or onlyoutlined in brief, and thus may be not drawn to scale. The same orsimilar reference symbols are used throughout the drawings to refer tothe same or like parts.

The aspects, features and advantages of the present invention will bemore apparent from the following detailed description taken inconjunction with the accompanying drawings. The description of thevarious embodiments is to be construed as exemplary only and does notdescribe every possible instance of the present invention. It should beapparent to those skilled in the art that the following description ofvarious embodiments of the present invention is provided forillustration purpose only and not for the purpose of limiting thepresent invention as defined by the appended claims and theirequivalents. The same reference symbols are used throughout thedescription to refer to the same parts.

Meanwhile, it is known to those skilled in the art that blocks of aflowchart (or sequence diagram) and a combination of flowcharts may berepresented and executed by computer program instructions. Thesecomputer program instructions may be loaded on a processor of a generalpurpose computer, special purpose computer or programmable dataprocessing equipment. When the loaded program instructions are executedby the processor, they create a means for carrying out functionsdescribed in the flowchart. As the computer program instructions may bestored in a computer readable memory that is usable in a specializedcomputer or a programmable data processing equipment, it is alsopossible to create articles of manufacture that carry out functionsdescribed in the flowchart. As the computer program instructions may beloaded on a computer or a programmable data processing equipment, whenexecuted as processes, they may carry out steps of functions describedin the flowchart.

A block of a flowchart may correspond to a module, a segment or a codecontaining one or more executable instructions implementing one or morelogical functions, or to a part thereof. In some cases, functionsdescribed by blocks may be executed in an order different from thelisted order. For example, two blocks listed in sequence may be executedat the same time or executed in reverse order.

In the description, the word “unit”, “module” or the like may refer to asoftware component or hardware component such as an FPGA or ASIC capableof carrying out a function or an operation. However, “unit” or the likeis not limited to hardware or software. A unit or the like may beconfigured so as to reside in an addressable storage medium or to driveone or more processors. Units or the like may refer to softwarecomponents, object-oriented software components, class components, taskcomponents, processes, functions, attributes, procedures, subroutines,program code segments, drivers, firmware, microcode, circuits, data,databases, data structures, tables, arrays or variables. A functionprovided by a component and unit may be a combination of smallercomponents and units, and may be combined with others to compose largecomponents and units. Components and units may be configured to drive adevice or one or more processors in a secure multimedia card.

In the description, the “state model” is generated by learning variousdata logs in the past that determine the state of the device, such asthe resource usage state or the power usage state. The state model mayoutput a prediction result for the state of the device in the futurewhen a certain amount or more of data logs is input.

In the description, the “state related information” refers toinformation on the factors that determine the state (e.g., softwarestate or hardware state) of the device. The state related informationmay be derived from the state model generated based on the learning datafor the state. The state related information may include at least oneparameter determining the state, and may further include weightinformation indicating the extent to which the parameters determine thestate.

In the description, the “model related information” may includeinformation regarding the characteristics of data logs, the algorithm,and the parameters used to generate the state model in the device, orinformation on the accuracy of the state model.

FIG. 3 is a block diagram illustrating the internal structure andoperation of a device according to an embodiment of the presentinvention.

More specifically, the device may include a modeling unit 310, aprediction unit 320, a control unit 330, and a remote communication unit340. The data log 300 can be input to the device and optimized deviceconfiguration information 350 can be output. That is, it can be seenthat the control unit 330 is newly added to the existing predictionmodel approach. Hereinafter, to distinguish the device from a differentor external device that exchanges information with the device throughthe remote communication unit, the device including the control unit 330is referred to as a local device, and the other device is referred to asa remote device.

The modeling unit 310 may learn data logs from the control unit 330 togenerate the state model of the local device. In this case, the modelingunit 310 can generate the state model of the local device using staterelated information and model related information received through theremote communication unit 340 from the remote device.

The prediction unit 320 may produce a prediction result for each statemodel on the basis of a preset amount or more of data logs and at leastone state model from the control unit 330. That is, the prediction unit320 may input received data logs to the state model generated in thelocal device and the state model generated in the remote device toproduce prediction results for each state model.

The control unit 330 may cause the modeling unit 310 and the predictionunit 320 to work together and exchange information with the remotedevices through the remote communication unit 340. That is, the controlunit 330 can act as an integrator for state prediction in the device.Specifically, the control unit 330 may include a monitoring part 331, aparameter selector 333, a prediction result determiner 335, and aconfiguration change determiner 337.

The monitoring part 331 collectively manages the interfaces between theinternal modules of the control unit 330 and may continuously monitorthe status of the local device. The monitoring part 331 may store datalogs 300 obtained from the sensors attached to the local device and maystore the state model received from the modeling unit 310. The statemodel may include state related information determining the state, andmay be composed of parameters and weighting information for theparameters. For example, the state model may include information on theparameters, arranged in order of weight, which determine the state ofthe device.

The parameter selector 333 may select a parameter to be shared with atleast one remote device according to a preset criterion from among oneor more parameters included in the state model generated by the modelingunit 310. Here, the parameter selector 333 can dynamically determine thenumber of parameters to be shared based on the weight and the accuracyof the state model. The accuracy of the state model indicates the degreeof agreement between the predicted result calculated by entering thedata log into the state model and the actual result, and can be includedin the model related information. An example of the state model is shownin FIG. 7 , and a more detailed description is given with reference toFIG. 8 .

The prediction result determiner 335 may use both the prediction resultcalculated based on the state model generated by the modeling unit 310of the local device and the prediction result calculated based on thestate model generated in the remote device to produce a predictionresult for the state of the local device. Also, the prediction resultdeterminer 335 may use both the accuracy of the state model of the localdevice and the reliability of the state model of the remote device toproduce a prediction result for the state. The accuracy and reliabilityof the state model indicate the degree of agreement between thepredicted result and the actual result for an input value to the statemodel. In the description, the term “accuracy” is used for the statemodel generated in the local device, and the term “reliability” is usedfor the state model generated in the remote device. A detaileddescription is given of the prediction result determiner 335, whichproduces a prediction result based on the state model of the localdevice and the state model of the remote device, with reference to FIG.10 .

The configuration change determiner 337 may generate a feedback valueusing the prediction result and the reliability thereof produced by theprediction result determiner 335 and determine whether to change theconfiguration of the device based on the feedback value. A detaileddescription is given of determining whether to change the configurationof the device with reference to FIG. 11 .

It is possible for the constituent modules of the control unit 330 toperform the above operations. However, it is well known in the art thatthe control unit 330 can directly perform all of the above operations.

The remote communication unit 340 may be connected to at least oneremote device to share state related information and model relatedinformation with the remote device. More specifically, the remotecommunication unit 340 may transmit at least one remote device aparameter selected based on the state model. The remote communicationunit 340 may transmit the weight information of the parameter and theaccuracy information of the state model to the at least one remotedevice. That is, the remote communication unit 340 does not transmit adata log itself collected from the local device, but transmits the staterelated information, thereby consuming a smaller amount of resources.

Also, the remote communication unit 340 may receive state-relatedinformation and model-related information from at least one remotedevice. The control unit 330 may receive state related information andmodel related information through the remote communication unit 340 fromthe remote device, construct a model of the remote device based on thereceived information, and use the constructed model to produce aprediction result for the local device.

That is, the remote communication unit 340 may receive, from at leastone remote device, model related information including a parameterselected based on the state model of the remote device and weightinformation of the selected parameter, and state related informationincluding the reliability information of the state model.

The control unit 330 may finally produce an optimized deviceconfiguration 350 according to the predicted state of the device byusing the constituent modules thereof.

The controller 330 may control generating a state model of the devicebased on state related data, selecting at least one parameter thatdetermines the state of the device based on the generated state model,and transmitting the selected at least one parameter to at least onedifferent device.

The control unit 330 may further control transmitting weight informationcorresponding to the selected at least one parameter to the differentdevice. The control unit 330 may control transmitting only the parameteramong the state related data and the parameter to at least one differentdevice. The control unit 330 may control receiving, from at least onedifferent device, at least one parameter, which determines the state ofthe different device and is selected based on the state model generatedin the different device, and the weight information corresponding to theselected parameter, and producing a prediction result for the state ofthe device on the basis of at least one parameter determining the stateof the device and at least one parameter determining the state of thedifferent device.

The control unit 330 may control receiving, from at least one differentdevice, information about the reliability of the prediction resultderived from the state model generated in the different device. Thecontrol unit 330 may control producing a prediction result for the stateof the device in consideration of the accuracy of the prediction resultderived from the state model generated in the device and the reliabilityof the prediction result derived from the state model generated in thedifferent device. In addition, the control unit 330 may controldetermining whether to change the configuration of the device based onthe prediction result for the state.

FIG. 4 illustrates pieces of information transmitted and receivedbetween the internal components of the device according to an embodimentof the present invention.

More specifically, the device may further include interface mechanismsfor transmitting and receiving information between the modeling unit410, the prediction unit 420, and the control unit 430.

First, the interface for transmitting information from the control unit430 to the modeling unit 410 may be referred to as “SND_to_MU” 431. Thecontrol unit 430 may transmit data logs received from various sensors ofthe local device. Typically, the data log is streamed to the controlunit 430 as an input value and processed through a pre-processing step.Pre-processing is not within the scope of the present invention and isnot described herein. In addition, the control unit 430 may transmitstate related information and model related information received throughthe remote communication unit 440. The state related information mayinclude parameters selected based on the state model generated at theremote device and weights for the parameters. The model relatedinformation may include information regarding characteristics of datalogs, algorithms, and parameters used to generate the state model. Themodel related information may also include information on thereliability of the state model. The modeling unit 410 can learn the datalogs received from the local device to generate a state model. Togenerate the state model, the modeling unit 410 may additionally use thestate related information and model related information received throughthe remote communication unit 440. The learning algorithms may include,for example, machine learning algorithms.

The interface for transmitting information from the modeling unit 410 tothe control unit 430 may be referred to as RCV_from_MU 433. The modelingunit 410 may transmit the generated state model and model relatedinformation to the control unit 430. The control unit 430 may derivestate related information from the state model. That is, the controller430 may produce information on the parameters determining the state andthe weight information for the parameters.

Next, the interface for transmitting information from the control unit430 to the prediction unit 420 may be referred to as SND_to_PU 435. Thecontrol unit 430 may transmit a given amount or more of data logs at thepresent time for predicting the state of the device as part ofSND_to_PU. The control unit 430 may transmit the state model receivedfrom the modeling unit 410, that is, a state model generated based onthe data logs collected from the local device and a state model receivedfrom the remote device. Upon receiving new data logs at the current timepoint, the state models, and the state related information, theprediction unit 420 can produce a prediction result on the state byapplying pre-stored algorithms to each state model. The algorithms mayinclude, for example, machine learning algorithms.

The interface for transmitting information from the prediction unit 420to the control unit 430 may be referred to as RCV_from_PU 437. Theprediction unit 420 may transmit a prediction result for the state ofthe device when each state model is applied. Thereby, the control unit430 can produce a prediction result for the state of the local device byusing both the prediction result derived from the state model of thelocal device and the prediction result derived from the state model ofthe remote device.

Next, the interface for transmitting information from the control unit430 to the remote communication unit 440 may be referred to asSND_to_Remote 438. The control unit 430 may send state relatedinformation to at least one remote device based on the state modelreceived from the modeling unit 410 through continuous monitoring. Theremote device may be a member of a group of devices havingcharacteristics similar to the local device. The control unit 430 mayselect state related information based on the received state model anddetermine whether to transmit the state related information to the atleast one remote device. This is described in detail with reference toFIG. 9 .

The interface for transmitting information from the remote communicationunit 440 to the control unit 430 may be referred to as RCV_from_Remote439. The remote communication unit 440 may transmit state relatedinformation based on the state model generated in the remote device tothe control unit 430. More specifically, the control unit 430 mayreceive parameters selected based on the state model and weightinformation indicating the degree to which the parameters determine thestate. The control unit 430 may also receive information on thereliability of the state model generated at the remote device. Thecontrol unit 430 may calculate a prediction result for the state usingthe received state related information. This is described in detail withreference to FIG. 10 . In addition, peer-to-peer (P2P) sharing schemesmay be used as distributed algorithms for transmitting and receivinginformation through the remote communication unit 440 to and from otherremote devices.

FIGS. 5A and 5B illustrate grouping of devices to share state relatedinformation according to an embodiment of the present invention.

More specifically, assuming that the device is a base station, FIG. 5Ashows groups of base stations sharing state related information andmodel related information for the model on a map. A group of one or moredevices may be referred to as a shared model group. FIG. 5B is a tableshowing attribute values of base stations. Base stations can be groupedbased on the installation area, the software version, the number ofassigned cells, and the like to create shared model groups.

The group information can be determined by the base station managementsystem based on the attribute values of the base stations as shown inFIG. 5B. For example, a k-mode clustering technique may be performed togroup the base stations, and the information on the shared model groupsmay be notified to the base stations of the groups. Thereafter, the basestations belonging to the same shared model group may share staterelated information. FIGS. 6A and 6B illustrate producing a predictionresult for the device installation state and performing a feedbackoperation according to an embodiment of the present invention.

More specifically, FIG. 6A illustrates operations between internalmodules in a newly installed local device, and FIG. 6B illustratesoperations between internal modules in a previously installed localdevice.

In the case of a newly installed local device of FIG. 6A, there are fewor no accumulated data logs. Hence, the control unit 610 may receive thestate related information and model related information through theinterface connected to the remote communication unit 600(RCV_from_Remote 605) from the remote devices belonging to the sameshared model group, and produce a prediction result. That is, thecontrol unit 610 can receive the state related information selected fromthe remote base station through the remote communication unit 600. Thecontrol unit 610 may transmit a new data log at the current local basestation and the state related information received from at least oneremote base station to the prediction unit 625 through SND_to_PU 620.The prediction unit 625 can produce a prediction result based on thereceived new data log at the current time point and the state relatedinformation of at least one remote base station, and transmit theprediction result back to the control unit 622 via RCV_from_PU 627. Thecontrol unit 610 can utilize the prediction result produced by at leastone remote base station and the reliability of the state model at theremote base to produce a prediction result at the current time point inthe local base station, and perform a feedback operation (630). Thefeedback operation may include determining whether to change the currentconfiguration of the device.

In the case of a previously installed local device of FIG. 6B, there areaccumulated data logs. Hence, the local device may share state relatedinformation derived from the state model generated based on theaccumulated data logs therein with at least one remote device. Inaddition, the local device may produce a prediction result at thecurrent time in the local device on the basis of the state relatedinformation received from the remote device and the state modelgenerated in the local device.

First, a description is given of sharing state related informationderived from the state model generated in the local device with at leastone remote device. The control unit 655 can transmit the accumulateddata logs to the modeling unit 665 via SND_to_MU 660. The modeling unit665 can generate a state model based on the data logs and transmit thestate model back to the control unit 655 via RCV_from_MU 667. Thecontrol unit 655 may transmit the state model generated based on thedata logs to the prediction unit 675 through SND_to_PU 670. In thiscase, the control unit 655 may also transmit a new data log collected atthe current time point. The prediction unit 675 can produce a predictionresult based on the new data log and the state model.

The prediction unit 675 can transmit the prediction result to thecontrol unit 655 via RCV_from_PU 677. The control unit 655 can computethe prediction accuracy of the state model on the basis of theprediction result derived from the state model generated based on thedata logs of the local device. Obtaining the prediction accuracy is notwithin the scope of the present invention, and a description thereof isomitted. Based on the prediction accuracy of the state model, thecontrol unit 677 may select parameters to be shared with the remotedevice from among the parameters included in the state relatedinformation of the state model, and determine whether to share the staterelated information of the state model. This is described in more detailwith reference to FIG. 8 . Upon determining to share the state modelgenerated in the local base station and selecting the parameters to beshared among the state related information, the control unit 655 maytransmit the selected parameters and weight information corresponding tothe selected parameters to the remote communication unit 640 throughSND_to_Remote 680.

Next, a description is given of producing a prediction result at thecurrent time point in the local device based on the state modelgenerated in the local device. The control unit 655 can receive staterelated information and model related information about the state modelcreated in the remote device from the remote communication unit 640 viaSND_to_Remote 680. The description on creating the state model andselecting the parameters to be shared in the local device is the same asthat in the remote device. The control unit 655 may forward the staterelated information received from the remote device to the predictionunit 675 via SND_to_PU 670. At the same time, the control unit 655 canalso transmit a certain amount of data logs collected by the localdevice up to the current time point.

The predicting unit 675 can produce a prediction result for the datalogs at the current time point by using at least one parameter includedin the state related information and a weight corresponding to theparameter. The prediction unit 675 can transmit the prediction resultbased on the state related information received from the remote deviceto the control unit 655 through RCV_from_PU 677. The control unit 655can utilize both the prediction result based on the state model of thelocal device and the prediction result based on the state model of theremote device to produce the prediction result for the state of thelocal device. This is described in more detail with reference to FIG. 10. The control unit 655 may perform a feedback operation based on theproduced prediction result (690). Specifically, the control unit 655 maydetermine whether to perform a feedback operation based on theprediction result.

FIG. 7 shows a graph representing the state model that predicts thedevice state.

More specifically, FIG. 7 illustrates a state model generated by themodeling unit based on data logs collected in the device. The device mayselect parameters to be shared with a remote device based on the statemodel. In the graph for the state model, the x-axis indicates the ranksof parameters (705) and the y-axis indicates the cumulative sum ofweights of the parameters (700). The parameters refers to factors thatdetermine the state of the device. For example, to make a prediction onthe network throughput of the device, the parameters may includereference signal received power (RSRP), reference signal receivedquality (RSRQ), and channel quality indication (CQI).

In FIG. 7 , the graph of cumulative weights of the parametersdetermining the state is in the form of a cumulative distributionfunction (CDF) graph. It can be seen that the slope of the graphdecreases as the number of parameters is accumulated according to theorder of weighting (710, 720, 730). The device may determine the numberof parameters to be shared on the basis of the slope of the graph. Thisis described in more detail with reference to FIG. 8 .

FIGS. 8A, 8B and 8C illustrate operations for selecting parameters to beshared with another device for a state model and determining whether toshare the state model according to an embodiment of the presentinvention.

More specifically, the device may receive data logs associated with thestate from the sensors (800). The device may generate a state modelbased on the data logs, and may identify parameters that determine thestate according to the state model and weights of the parameters (805).Here, the state model may be the one shown in FIG. 7 , and it is assumedin the following description that the state model is the same as thegraph shown in FIG. 7 .

The device may set N_(pre) to 0 for N indicating the number ofparameters (807). The device may set N to 1 (810). To compute the angledifference in the graph between the parameters of adjacent ranks, thedevice may calculate the angle difference in the graph between theN^(th) parameter and the N+1^(th) parameter (degree=I_(N)−I_(N+1))(820).

The device may then determine whether the calculated angle difference isless than a preset threshold of the slope (threshold_(D)) (823). If thecalculated angle difference is not less than threshold_(D), the devicemay increase the number of parameters by one (825), and repeat steps 820and 823. The device repeats steps 820 through 825 until the condition ofstep 823 is satisfied, and it can determine the number of parameterswhose weight is greater than or equal to a preset value. If thecalculated angle difference (I_(N)−I_(N+1)) is less than threshold_(D),the device may learn the first to N^(th) parameters to thereby producethe prediction result and the prediction accuracy (830). Thereafter, thedevice may determine whether the produced prediction accuracy is higherthan a preset threshold of the prediction accuracy (threshold_(P))(833). If the prediction accuracy is lower than threshold_(P), thedevice may determine whether the value of N is equal to the total numberof parameters (835). If N is equal to the total number of parameters,the device may determine the state model as unusable (837). That is, ifthe prediction accuracy is lower than threshold_(P) although all theparameters in the state model are used for producing the predictionresult, the state model is not shared with remote devices and is notused to make a prediction on the state in the local device.

If N is not equal to the total number of parameters, the device maydetermine whether flag is set to 1 (840). If flag is set to 1, the stepsize serving as the adjustment interval for threshold_(D) can be halved(843). Otherwise, step 840 can be skipped. Here, the step size means theinterval value that changes the number of parameters in order toidentify the minimum number of parameters whose prediction accuracysatisfies threshold_(P). Thereafter, the device may adjust threshold_(D)downward by subtracting the step size from threshold_(D) (845). This isto increase the number of parameters to be learned by loweringthreshold_(D) when the device has learned N parameters and produced aprediction result at step 830 with the prediction accuracy lower thanthreshold_(P). Then, the device may set flag to 0 and set N_(pre) to N(847).

Thereafter, steps 810 to 830 may be repeated to determine the number ofparameters based on changed threshold_(D).

On the other hand, if the prediction accuracy is higher thanthreshold_(P) at step 833, the device may determine whether N is equalto N_(pre) (850). If N is equal to N_(pre), the device may determine thecurrent state model as a usable state model (855). If N is not equal toN_(pre), the device may determine whether flag is set to 0 (860). If thedevice initially tests step 833, N_(pre) is 0 at step 850, which is thevalue set at step 807; the result of step 850 is always “no” even if theresult exceeds the prediction accuracy in the first attempt at step 833;and the device may initiate learning again by decreasing the number ofparameters through adjustment of threshold_(D). If the device previouslytested step 833 and passed the step 847 or 870, N_(pre) is the number ofparameters learned in the previous stage; and if N is equal to N_(pre)at step 850 after adjusting threshold_(D), that is, when the number oflearned parameters equals the number of learned parameters in theprevious stage, the device can determine the current state model as ausable state model.

Thereafter, if flag is set to 0 at step 860, the device may reduce thestep size serving as the adjustment interval for threshold_(D) by half(863). If flag is set to 1, the device can maintain the step size.

Thereafter, the device may adjust threshold_(D) upward by adding thestep size to threshold_(D) (865). This is to decrease the number ofparameters to be learned by increasing threshold_(D) when the device haslearned N parameters and produced a prediction result at step 830 withthe prediction accuracy higher than threshold_(P). Then, the device mayset flag to 1 and set N_(pre) to N (870). Thereafter, step 810 andsubsequent steps may be repeated to determine the number of parametersbased on changed threshold_(D).

Here, flag is a criterion for determining whether the step size foradjusting threshold_(D) is halved. When threshold_(D) is adjusteddownward, flag is set to 0 (847); and when threshold_(D) is adjustedupward, flag is set to 1 (870). If flag is 1 before threshold_(D) isadjusted downward, threshold_(D) is adjusted upward. At this time, thestep size is reduced by half. If flag is 0 before threshold_(D) isadjusted upward, threshold_(D) is adjusted downward. At this time, thestep size is reduced by half. That is, when the device adjuststhreshold_(D) differently from the previous stage, it can reduce thestep size by half.

Through the above process, the device can select a minimum number ofparameters reaching the desired prediction accuracy. For example, afterselecting 50 parameters based on initially determined threshold_(D) andlearning, if the prediction accuracy satisfies threshold_(P),threshold_(D) is increased to reduce the number of selected parameters.Thereafter, the device may learn 45 parameters (excluding 5 parameters)based on upwardly adjusted threshold_(D) and calculate the predictionresult. If the prediction accuracy of the prediction result does notsatisfy threshold_(P), the device can select an increased number ofparameters and learn again by adjusting threshold_(D) downward.

FIG. 9 illustrates the operation of determining whether to share staterelated information with another device according to an embodiment ofthe present invention.

The device may determine whether the state model is updated (900). Thatis, the device can determine whether a new state model has been createdusing newly accumulated data logs. Thereafter, the device may determinewhether the newly generated state model is a usable model (910). Thatis, the device can perform the operation described in FIG. 8 on thenewly generated state model to determine whether it is a usable model.If the newly generated state model is not a usable model, it is notshared and the procedure proceeds to step 900, at which the device maycheck whether a new state model is generated. If the newly generatedstate model is a usable model, the device may share the parametersselected from the state model with a remote device via the remotecommunication unit (920).

FIG. 10 illustrates an operation by which the device produces aprediction result on the state according to an embodiment of the presentinvention.

More specifically, the device can produce the final prediction result ofthe local device on the basis of the reliability or accuracy of thestate models generated in the local device and the remote device. Thatis, a state model with a high reliability or accuracy has a largeinfluence on the final prediction result, and a state model with a lowreliability or accuracy has a small influence on the final predictionresult. In addition, if the reliability of a state model does not exceeda preset threshold, it may be regarded as an inaccurate model and be notused for calculating the prediction result.

The device may obtain state related information, model relatedinformation, and state prediction results from the local device and theremote device (1000). The device may generate a state model based onlocally collected data logs, and use the prediction unit to calculate aprediction result based on the state related information of the statemodel. In addition, the device may receive state related information ofthe state model generated in the remote device, and may use theprediction unit to calculate a prediction result based on the staterelated information of the remote device.

Then, the device may set initial values by setting w (cumulative weight)to 0, setting N (index of a state model) to 1, and setting p (cumulativeprediction result) to 0 (1010). Among the state models generated in thelocal device and the remote device, the device may utilize a state modelwhose reliability is greater than threshold_(R) (preset threshold forreliability) only. The device may determine whether the reliability (oraccuracy) of model N is greater than threshold_(R) (1020). If thereliability of model N is less than threshold_(R), the device mayincrease the model index by one (1025) and may determine whether thereliability of the next model is greater than threshold_(R) (1020).

If the reliability of model N is greater than threshold_(R) at step1020, the device may compute the cumulative prediction result p (1030).That is, p=p+reliability of model N* prediction result of model N.Thereafter, the device may calculate the cumulative weight w (1040).That is, w=w+reliability (accuracy) of model N.

Then, the device may determine whether the model index N is equal to thetotal number of models corresponding to the state related informationobtained at step 1000 (1050). If N is less than the total number ofmodels, the device may increase the model index by one (1025), and theprocedure returns to step 1020 for the next model. Thereafter, if thecumulative prediction result p and the cumulative weight w aredetermined based on the reliability of all the models, the device maycalculate the final prediction result using p/w (1060).

For example, assume that the reliability of [model 1, model 2, model 3]is [0.9, 0.2, 0.1] and that the prediction result is [0.3, 0.9, 0.8].Assume that threshold_(R) is 0.1. Here, model 1 has a reliability muchhigher than threshold_(R), and it has a large influence on calculatingthe prediction result. Model 2 has a reliability a little higher thanthreshold_(R), and it has a small influence on calculating theprediction result. Model 3 has a reliability not higher thanthreshold_(R), and it has no influence on calculating the predictionresult. In this case, the cumulative prediction result p may be computedas shown in Equation 1 below.

p=0.9(reliability of model 1)×0.3(prediction result of model 1)+0.2(reliability of model 2)×0.9(prediction result of model2)=0.45  Equation 1

The cumulative weight w may be computed as shown in Equation 2 below.

w=0.9(reliability of model 1)+0.2(reliability of model 2)=1.1  Equation2

Hence, the final prediction result (p/w) is 0.41.

As described above, the final prediction result is 0.41. It can be seenthat model 1 with higher reliability than model 2 affects the predictionresult and the final prediction result is closer to the predictionresult (0.3) of model 1 than the prediction result (0.9) of model 2.

FIG. 11 illustrates a feedback operation performed by the device basedon the prediction result according to an embodiment of the presentinvention.

The device can generate a feedback value using the state predictionresult of the local device and the reliability of the prediction resultin FIG. 10 , and determine whether to execute the feedback operationusing the feedback value. Whether to execute the feedback operation mayinclude determining whether to change the configuration of the device.

More specifically, the device can obtain the state prediction result andthe reliability of the prediction result of the local device derived asin FIG. 10 (1100). The reliability of the prediction result can beobtained by dividing the cumulative weight w by the number of models.Thereafter, the device may determine a feedback value f (1110). Thefeedback value f is a criterion value for the device to determinewhether to perform the feedback operation. The device can determine thefeedback value f using Equation 3 below.

Feedback value(f)=prediction result×reliability of predictionresult+(1−prediction result)×(1−reliability of predictionresult)  Equation 3

The device may determine whether the determined feedback value f isgreater than or equal to preset threshold_(f) (1120). If the feedbackvalue f is less than threshold_(f), the procedure can be terminatedwithout changing the configuration of the device. If the determinedfeedback value f is greater than or equal to threshold_(f), the devicemay change the configuration of the device based on the feedback value f(1130).

For example, assume that the device is a base station and the failurestate of a fan is to be predicted. When the prediction result for thefailure of the fan is 0.9, the reliability of the prediction result is0.8, and threshold_(f) is 0.7, the feedback value f is given as follows.

$\begin{matrix}{f = {{0.9*0.8} + {\left( {1 - 0.9} \right)*\left( {1 - 0.8} \right)}}} \\{= 0.74}\end{matrix}$

As this value is greater than threshold_(f), the device can determinethat a fan failure will occur with a high probability and determine tochange the hardware or software settings of the base station. Bydetermining the feedback value using the reliability of the currentprediction result, the device can predict and prepare for a moreaccurate state in real time.

The above-described method of sharing state related informationincluding parameters selected based on the state model between deviceshaving similar characteristics, and predicting the state of the localdevice based on the shared state related information can be applied asfollows.

For example, to distribute network resources in real time by using thepresent invention, base stations installed in the same cell site, i.e.,the same physical area, may monitor their available resources andterminal traffic. If the terminal traffic amount exceeds a giventhreshold, the base stations may perform resource migration. In thisway, the base station can keep the quality of experience (QoE) of theuser at a high level. By using the information about small statesrelated to component resources of the base station, it is possible topredict and prepare for a large problem in advance while operating thenetwork apparatus. As a result, it is possible to reduce the operationcost of the base station and to maintain the stability of the apparatus.

To optimize the power consumption of the base stations by utilizing thesmart grid technology, the present invention can be utilized tointroduce a rechargeable battery structure according to thepeak/off-peak hours and the hourly power charges. More specifically, aparameter can be used for the number of terminals served per hour. Inaddition, parameters for physical resource block (PRB) usage amount,terminal data throughput, cell load, hardware component load, andbackhaul load can be used. Additionally, parameters for the number ofneighbor base stations can be used to account for the power consumedwhen the base station uses interference control modules. As the strengthof a signal transmitted to the terminal can be influenced by the channelcondition, parameters for the channel state can be used. The patterns ofpower consumption (voltage, ampere, ohm, interference) can be predictedbased on the above parameters, and base station resources (radio power,frequency bandwidth) can be changed as needed based on the predictedpower usage patterns. Hence, it is possible to operate fewer basestations, to reduce unnecessary facility investment, and to reduce thepower operation cost as needed.

To optimize the network throughput performance of terminals, the basestation may monitor radio information (e.g., reference signal receivedpower (RSRP), reference signal received quality (RSRQ), channel qualityindication (CQI)) and expected quality of experience (QoE). Thereby,performance of the terminals can be optimized through effectivelyallocated resources. Here, expected radio information and quality ofexperience (QoE) values of the terminals can be determined according tothe billing plan, terminal type, and traffic pattern. For example, ifthe user subscribed to an expensive billing plan requires a terminalwith a modem chipset supporting the high capacity downlink and a lowlatency application service, the base station can identify the availableresources and the expected resources in the future in advance andperform resource migration to the base station with the highestperformance.

To maintain the connectivity of the terminals, the high seamlesshandover rate, and high transparency, the present invention can be usedfor enabling the base stations connected with the terminals to predictsoftware and hardware states. The base station may monitor the operationof various software modules and hardware modules internally (e.g.,available resources and stability) and make a prediction on the normaloperation. If a particular component exhibits an anomalous condition(e.g., optical error, communication error, memory error, fan error,memory full, CPU (central processing unit) full, DSP (digital signalprocessing) error), the base station can handover the terminals to anormally operating base station.

In the 5G communication system, a next-generation network becomingpopular in recent years, to utilize network function virtualization(NFV) technology in conjunction with software defined networking (SDN)technology, the present invention may be applied to flexibly steeringnetwork traffic through the central SDN controller and to flexibly drivethe apparatus resources as needed through NFV technology. For example,deep packet inspection technology installed on a base station may beused based on data logs for the parameters such as bandwidth,throughput, latency, jitter, and error rate between base stations.Thereby, the user application content and association patterns can beextracted. The extraction results can be sent to the SDN controller,which may result in higher user QoS and QoE and reduced operator capitalexpenditure and operational expenditure (CapEx/OpEx).

In the embodiments described above, all steps and messages may besubject to selective execution or may be subject to omissions. The stepsin each embodiment need not occur in the listed order, but may bereversed. Messages need not necessarily be delivered in order, but theorder may be reversed. Individual steps and messages can be performedand delivered independently.

Some or all of the table in the above-described embodiments is shown toillustrate embodiments of the present invention for facilitatingunderstanding. Hence, the details of the table can be regarded asrepresenting a part of the method and apparatus proposed by the presentinvention. That is, semantical approach to the contents of the tableherein may be more desirable than syntactical approach thereto.

Hereinabove, various embodiments of the present invention have beenshown and described for the purpose of illustration without limiting thesubject matter of the present invention. It should be understood bythose skilled in the art that many variations and modifications of themethod and apparatus described herein will still fall within the spiritand scope of the present invention as defined in the appended claims andtheir equivalents.

1. A method performed by a device of a base station (BS), the methodcomprising: obtaining local data and a state model for local learning topredict a state of the device; performing the local learning to updatethe state model using the local data based on a machine learningalgorithm; obtaining information for determining the state of the deviceusing the updated state model, based on the local learning; andtransmitting, to a remote device through communication circuitry of thedevice, first state related information including the information fordetermining the state of the device using the updated state model,wherein the state of the device is associated with at least one of apower consumption of the device, a resource usage of the device relatedto frequency or time resources, an abnormal operation of the BS fordetecting at least one error occurring in the device, or a networkthroughput performance of at least one terminal.
 2. The method of claim1, further comprising: receiving, from the remote device through thecommunication circuitry of the device, second state related informationfor predicting the state of the device.
 3. The method of claim 2,wherein the performing of the local learning comprises: performing thelocal learning to update the state model based on the second staterelated information.
 4. The method of claim 2, wherein the performing ofthe local learning further comprises: updating the state model based onthe first state related information and the second state relatedinformation.
 5. The method of claim 1, wherein the information fordetermining the state of the device comprises at least one parameter forupdating the state model among a plurality of parameters for the statemodel, and wherein the number of the at least one parameter is smallerthan a total number of the plurality of parameters for the state model.6. The method of claim 5, wherein the information for determining thestate of the device comprises weight information of the at least oneparameter, and wherein the at least one parameter and the weightinformation are used to determine the state of the BS based on theupdated state model in the remote device.
 7. The method of claim 1,wherein the transmitting of the first state related informationcomprises: determining whether the remote device belongs to a sharedgroup for the device; and in case that the remote device belongs to theshared group, transmitting the first state related information to theremote device.
 8. The method of claim 1, wherein the at least one errorcomprises at least one of a communication error, a memory error, a fanerror, a memory full error, a central processing unit (CPU) full error,or a digital signal processing (DSP) error.
 9. The method of claim 1,further comprising: receiving, from the remote device through thecommunication circuitry of the device, information associated with thestate model for the local learning in the device.
 10. The method ofclaim 1, wherein the local data comprises sensor data.
 11. A device of abase station (BS), comprising: communication circuitry; and a processorconfigured to: obtain local data and a state model for local learning topredict a state of the device, perform the local learning to update thestate model using the local data based on a machine learning algorithm,obtain information for determining the state of the device using theupdated state model based on the local learning, and control thecommunication circuitry to transmit, to a remote device, first staterelated information including the information for determining the stateof the device using the updated state model, wherein the state of thedevice is associated with at least one of a power consumption of thedevice, a resource usage of the device related to frequency or timeresources, an abnormal operation of the BS for detecting at least oneerror occurring in the device, or a network throughput performance of atleast one terminal.
 12. The device of claim 11, wherein the processor isfurther configured to control the communication circuitry to receive,from the remote device, second state related information for predictingthe state of the device.
 13. The device of claim 12, wherein, to performthe local learning, the processor is configured to: perform the locallearning to update the state model based on the second state relatedinformation.
 14. The device of claim 12, wherein, to perform the locallearning, the processor is configured to: update the state model basedon the first state related information and the second state relatedinformation.
 15. The device of claim 11, wherein the information fordetermining the state of the device comprises at least one parameter forupdating the state model among a plurality of parameters for the statemodel, and wherein the number of the at least one parameter is smallerthan a total number of the plurality of parameters for the state model.16. The device of claim 15, wherein the information for determining thestate of the device comprises weight information of the at least oneparameter, and wherein the at least one parameter and the weightinformation are used to determine the state of the BS based on theupdated state model in the remote device.
 17. The device of claim 11,wherein, to transmit the first state related information, the processoris configured to: determine whether the remote device belongs to ashared group for the device, and in case that the remote device belongsto the shared group, transmit the state related information to theremote device.
 18. The device of claim 11, wherein the at least oneerror comprises at least one of a communication error, a memory error, afan error, a memory full error, a central processing unit (CPU) fullerror, or a digital signal processing (DSP) error.
 19. The device ofclaim 11, wherein the processor is further configured to control thecommunication circuitry to receive, from the remote device, informationassociated with the state model for the local learning in the device.20. An electronic device comprising: a memory configured to storeinstructions, wherein, when the instructions are executed on a device ofa base station (BS), the instructions cause the device to: obtain localdata and a state model for local learning to predict a state of thedevice, perform the local learning to update the state model using thelocal data based on a machine learning algorithm, obtain information fordetermining the state of the device using the updated state model basedon the local learning, and transmit, to a remote device throughcommunication circuitry of the device, first state related informationincluding the information for determining the state of the device usingthe updated state model, and wherein the state of the device isassociated with at least one of a power consumption of the device, aresource usage of the device related to frequency or time resources, anabnormal operation of the BS for detecting at least one error occurringin the device, or a network throughput performance of at least oneterminal.